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This chapter uses advance order data from an actual manufacturing shop to develop and test a forecast model for total demand. The proposed model made direct use of historical time series data for total demand and time series data for advance orders. Comparison of the proposed model to commonly used approaches showed that the proposed model exhibited greater forecast accuracy.

Perhaps the most popular pricing model among Bitcoin enthusiasts is the stock-to-flow (S2F) model. The model gained significant traction after successfully predicting the meteoric rise of Bitcoin prices from late 2020 to early 2021. This paper dissects the S2F model for Bitcoin empirically to determine its viability and investigate whether investors can profit from an S2F-based trading strategy.

This paper, dissects the S2F model for Bitcoin by putting it through a battery of tests to examine its design, characteristics, robustness and appropriateness.

Overall, this paper finds the S2F model to be insensitive to differing assumptions in the early stages of the model, alleviating concerns about data mining. This paper produces a dynamic S2F model with no peek-ahead bias and shows evidence that prediction accuracy increases over time. Finally, this paper shows that a dynamic trading strategy that goes long (short) when Bitcoin is undervalued (overvalued) according to S2F is far less profitable than a classic buy-and-hold strategy.

To the best of the authors’ knowledge, this is the first paper to analyze the S2F model in an academic setting by providing a rigorous assessment of the model's construction. This paper demonstrates how the model can be implemented realistically without the peek-ahead bias, creating a tool that can be used contemporaneously by investors.

The purpose of this paper is to examine whether earnings quality attributes are reflected in AM best's financial strength ratings (FSRs), a measure widely used in the insurance industry to assess financial health.

Using a sample of insurance companies during the period 2006-2012, the authors measure the quality of reported earnings using three accounting-based measures: earnings persistence, accrual quality, and earnings smoothness.

The authors find that better earnings persistence, higher accrual quality, and less earnings smoothing are reflected in higher FSRs for both public and private insurers, with the magnitude of the effect greater for private insurers.

This is the first study of which the authors are aware that seeks to understand the impact, if any, of variations in the quality of reported financial information on the perceived financial health of firms by ratings agencies in the insurance industry. The authors also include a novel research design in assessing the determinants of financial health ratings. Users of FSRs should be aware of the impact of ownership structure on ratings agencies’ propensity to incorporate reported earnings attributes in their ratings.

The purpose of this paper is to predict the population of Anhui province. The authors analyze the trend of the main demographic indicators.

On the basis of the main methods of statistics, this paper studies the tendency of the population of Anhui province. It mainly analyzes the sex structure and the age structure of the current population. Based on the GM(1,1) model, this paper forecasts the total population, the population sex structure, and the population age structure of Anhui province in the next ten years.

The results show that the total population was controlled well, but there have been many problems of the population structure, such as the aging population, high sex ratio, heavy social dependency burden, and the declining labor force.

This paper forecasts the main indexes of the population of Anhui province and provides policy recommendations for the government and the relevant departments.

This paper utilizes data analysis method and the grey forecasting model to study the tendency of the population problems in Anhui province.

In response to recent financial corporate scandals, this study aims to provide a helpful understanding for investors and accounting regulators on how firms manage their reported earnings. This leads to a better firm valuation by financial intermediaries and more useful accounting standards.

Estimating discretionary accruals and opportunistic special purpose entities and using a simultaneous equation approach, the aim is to check how managers trade off between such tools of earnings management. Based on real earnings manipulation and accruals management of earnings, the goal is to understand if such tools are used simultaneously or as substitute by firms.

After controlling for each cost determinants of such earnings management tool, firms use discretionary accruals and financial engineering with special purpose entities as substitutes. Additional analyses show that managers use such tools in a sequential process. Indeed, they first use special purpose entities during the course of the year but they manipulate discretionary accruals especially at the end of the year.

Despite sensitivity checks, measurement error in discretionary accruals proxy and opportunistic SPE estimation model remains an alternative explanation for the results. The sample size and the lack of accurate information about the size of special purpose entities may limit the extent of the findings.

It is a very useful tool for regulators when they plan to disclose new accounting standards. For investors, this study can help them in assessing the firm's value more accurately for investing and financing purposes.

Providing a new methodology and new models to detect pervasive earnings management strategies adopted by firms.

The purpose of this paper is to investigate the effectiveness of GM(1,1) model on linear growth sequences (LGS) by random experiments and global primary energy consumption is predicted as by the GM(1,1) and the autoregressive integrated moving average (ARIMA) model, which is used as a reference.

LGS generated randomly are used for GM(1,1) modeling. The results of the massive repeated random experiments are analyzed to test the effectiveness of the GM(1,1) model and global primary energy consumption is predicted using the GM(1,1) model and the ARIMA model.

The use of the GM(1,1) model is effective when used for a LGS and the model is proven to be reliable by the experiments. Global primary energy consumption is predicted with the GM(1,1) model and the ARIMA model as a case study, and the results show that GM(1,1) is quite good. Global primary energy consumption will increase by 1.03 percent in 2016.

The contribution of this paper includes the following: first, the applicability of the GM (1,1) model is further discussed with random experiments and it is feasible for a LGS; second, random experiments provide good proof that four data are enough for GM(1,1) modeling, and GM(1,1) model is reliable; third, prediction by using GM(1,1) model with small data is even better than time-series analysis in the case study.

The Indian stock market can be tricky when there's trouble in the world, like wars or big conflicts. It's like trying to read a secret message. We want to figure out what makes investors nervous or happy, because their feelings often affect how they buy and sell stocks. We're building a tool to make prediction that uses both numbers and people's opinions.

Hybrid approach leverages Twitter sentiment, market data, volatility index (VIX) and momentum indicators like moving average convergence divergence (MACD) and relative strength index (RSI) to deliver accurate market insights for informed investment decisions during uncertainty.

Our study reveals that geopolitical tensions' impact on stock markets is fleeting and confined to the short term. Capitalizing on this insight, we built a ground-breaking predictive model with an impressive 98.47% accuracy in forecasting stock market values during such events.

To the best of the authors' knowledge, this model's originality lies in its focus on short-term impact, novel data fusion and high accuracy. Focus on short-term impact: Our model uniquely identifies and quantifies the fleeting effects of geopolitical tensions on market behavior, a previously under-researched area. Novel data fusion: Combining sentiment analysis with established market indicators like VIX and momentum offers a comprehensive and dynamic approach to predicting market movements during volatile periods. Advanced predictive accuracy: Achieving the prediction accuracy (98.47%) sets this model apart from existing solutions, making it a valuable tool for informed decision-making.

The purpose of this paper is to introduce the new class ratio dispersion, the new smooth degree sequence and the comparison criterion of the new smooth degree and to propose the new prior check of grey modeling in order to meet the modeling demand of the optimized grey models which have the white exponential law of coincidence.

This paper introduces the corresponding new concepts and new comparison criterion which can reflect the approach degree of the raw data and the normal geometric progression by analogy with the traditional class ratio dispersion, smooth degree sequence and comparison criterion.

To the optimized grey models, the new concepts and the new comparison criterion can be regarded as the prior check of grey modeling.

First, the new concepts and the new comparison criterion can reflect the approach degree of the raw data and the normal geometric progression, and this paper proposes the prior check of grey modeling to the optimized grey models. Second, this paper proposes the quantificational valuation criterion – the concept of the smooth degree which can reflect the approach degree of a single sequence and the normal geometric progression, and ends the status quo that there is only the comparison criterion of the smooth degree between two sequences but not the quantificational valuation criterion of a single sequence.

The purpose of this paper is to utilize the proposed function transformation to make the original data series meet the properties of smooth ratio being lessen and stepwise ratio deviation being reduced, so that to improve the accuracy of grey forecasting model.

According to the characteristics of anti-cotangent functional graph variation, the theory of functional transformation and grey system modeling, the authors proposed a grey model based on the transformation of Aarc cot x+B function.

The calculated result of practical example shows that the proposed method is both valid on improving fitting effectiveness and forecasting accuracy.

The proposed method in this paper can effectively improve the accuracy of forecasting of high-growth original data series (derivative of data series is not only greater than 1 but also increasing).

The paper succeeds in providing an effective function transformation to make the smooth ratio and stepwise ratio deviation reduced significantly.

The purpose of this paper is to investigate the impact of regulations, reforms and legal environment on dividend policy in a different institutional setting. Particularly, it examines the firm-level cash dividend behaviour of publicly listed firms in Turkey in the post-2003 period, since there were major economic and structural reforms as well as significant regulatory changes of dividend payout rules imposed by the supervisory bodies.

The paper focuses on a recent large panel data set of 264 Istanbul Stock Exchange (ISE)-listed firms over a ten-year period 2003-2012. First, it employs a modified specification of Lintner’s (1956) partial adjustment model for analysis regarding target payout ratio and dividend smoothing. Second, it performs a logit model for analysis in identifying the link between financial characteristics and the likelihood of paying dividends.

The results show that ISE firms now follow the same determinants as suggested by Lintner. They, indeed, have long-term payout ratios and adjust their cash dividends by a moderate level of smoothing, and therefore adopt stable dividend policies (although less stable policies compared to their counterparts in the developed US market) as a signalling mechanism over the period 2003-2012. Moreover, the results also report that ownership structure concentration affects the target payout ratio and dividend smoothing in the Turkish market. In addition, the results further show that more profitable, more mature and larger sized ISE firms are more likely to pay cash dividends, whereas ISE firms with higher investment opportunities and more debt are less likely to distribute cash dividends in the post-2003 period.

To the best of authors’ knowledge, this paper is the first major research that examines the implications of reforms and regulations on cash dividend payments and dividend smoothing over time in Turkey during its market integration process in the post-2003 period.